P06-09 Data processing with the short questionnaire to assess health enhancing physical activity (SQUASH): an update

Abstract Background The Short Questionnaire to Assess Health Enhancing Physical Activity (SQUASH) is a widely used questionnaire, and used for monitoring prevalence rates of physical activity(PA) in the Netherlands. To provide a standardized protocol for data processing and analysis of the SQUASH, an analysis guide was published in 2004. However, since then, the compendium of Metabolic Equivalent (MET) values of PA has been updated, and new PA guidelines have been developed. The new PA guidelines differ from the old ones in terms of the appropriate amount of active time (150 minutes/week versus 5 days/week 30 minutes), decrease in cut-off point for moderate intensity (adults 18-54 years of age) and adding a bone- and muscle strengthening component. Therefore, the protocol for data processing and analysis of the SQUASH needs to be updated. In this study, results from the old and new protocol demonstrate the differences in adherence rates between the two sets of guidelines in the Netherlands for the adult population. Methods Data of a nationally representative sample of 6942 participants aged 18 years and older were used to calculate adherence to the old and the new PA guidelines by using the original and the updated protocol. In the new protocol, the MET-values of the activities including sports were adjusted according to the 2011 Compendium. Moderate intense activity was defined as ≥ 3.0 MET irrespective of age and the bone and muscle strengthening component was added. Results Adherence to the old Dutch PA guidelines is 48.1% among adults aged 18-54 years, and 74.4% among adults 55 years and older. For the new PA guidelines the adherence is 48.4% and 38.1% respectively. The large difference for adults 55 years and older is due to changes in the cut-off values for moderate-to-vigorous intensity PA and the addition of bone and muscle strengthening exercises. Conclusions The updated protocol for data processing and analysis of the SQUASH describes the steps to calculate the new PA guidelines in a structured way and gives researchers the opportunity to work with the data from the SQUASH in a uniform way. The SPSS syntax for data processing is available at: www.sportenbewegenincijfers.nl/methoden.


Background
Walking is a main form of Physical Activity (PA) and daily step counts have been used as a tool to objectively assess PA levels and patterns in many studies. Children who accumulate less than 9000 steps per day may be considered insufficiently active (Vale et al., 2015). The aim of the present study was to determine the importance of analysing all day data when evaluating PA recommendations.

Methods
The study sample comprised 202 preschool aged children (44% girls), aged from 3 to 6 years (mean age of 4,7AE0,8 years). Steps counts were measured during 7 consecutive days using waist worn, uniaxial Actigraph accelerometers (models 7164, 71256, and GT1M). Children used the accelerometer throughout the day, being placed after waking up and removed before going to bed. In addition to the number of steps throughout weekdays (monday to friday), the number of steps during school time was analyzed. The school hours were restricted to 8 hours and half, between 9:00h and 17:30h.

Results
During all day children account 10.563 steps, 6.947 of were recorded during school hours (p = 0.001). Looking entire weekday, we found that only 7% of preschool were considered insufficiently active (9.000 steps per Neverthless, looking for school hours only, we found that almost half of the sample (45%) met the same recommendation. We tested for differences between all day and school day with paired t-tests.

Conclusions
When looking for step counts across the entire day vs school hours, we announced that school hours, by itself, are not representative of the number of child's steps in each day.
analysing meet P06-08 Socio-demographic profile of physically inactive adults living in Italy according to the PASSI data Background Insufficient physical activity (PA) or physical inactivity (PI) is one of the ten leading risk factors for global mortality. PI leads to 20-30% increased risk of all-cause mortality and monitoring its current levels and trends in general population is essential to track progress towards health targets, identify at-risk groups, assess policies' effectiveness, guide future planning.

Background
The Short Questionnaire to Assess Health Enhancing Physical Activity (SQUASH) is a widely used questionnaire, and used for monitoring prevalence rates of physical activity(PA) in the Netherlands. To provide a standardized protocol for data processing and analysis of the SQUASH, an analysis guide was published in 2004. However, since then, the compendium of Metabolic Equivalent (MET) values of PA has been updated, and new PA guidelines have been developed. The new PA guidelines differ from the old ones in terms of the appropriate amount of active time (150 minutes/week versus 5 days/week 30 minutes), decrease in cut-off point for moderate intensity (adults 18-54 years of age) and adding a bone-and muscle strengthening component. Therefore, the protocol for data processing and analysis of the SQUASH needs to be updated. In this study, results from the old and new protocol demonstrate the differences in adherence rates between the two sets of guidelines in the Netherlands for the adult population.

Methods
Data of a nationally representative sample of 6942 participants aged 18 years and older were used to calculate adherence to the old and the new PA guidelines by using the original and the updated protocol. In the new protocol, the MET-values of the activities including sports were adjusted according to the 2011 Compendium. Moderate intense activity was defined as ! 3.0 MET irrespective of age and the bone and muscle strengthening component was added.

Results
Adherence to the old Dutch PA guidelines is 48.1% among adults aged 18-54 years, and 74.4% among adults 55 years and older. For the new PA guidelines the adherence is 48.4% and 38.1% respectively. The large difference for adults 55 years and older is due to changes in the cut-off values for moderate-tovigorous intensity PA and the addition of bone and muscle strengthening exercises.

Conclusions
The updated protocol for data processing and analysis of the SQUASH describes the steps to calculate the new PA guidelines in a structured way and gives researchers the opportunity to work with the data from the SQUASH in a uniform way. The SPSS syntax for data processing is available at: www.sporten-

Background
Walking or cycling regularly instead of using motorised vehicles returns benefits not only to our health but also to the environment: in Europe, during 2020 a spotlight has also been put on the importance of accessibility to zero-emission transport, for promoting an inclusive framework that involves everyone. Policies in favour of a diffused active mobility in the general population encourage also to take steps effectively in order to achieve the longer-term goal of a European continent that is carbon-neutral.

Methods
In the Italian Behavioural Risk Factor Surveillance System PASSI, active mobility identifies both adults (aged 18-69) who cycle or walk to go to work or to school or for their usual commuting and those who, thanks to this habit, reach out recommended levels of physical activity to gain health benefits.
Basing on their own active mobility levels, people are classified Background Regular physical activity (PA) has been found to be important for cardiovascular health and longevity. However, notable proportion of adult population does not meet the national PA recommendations. Active transport is one domain of physical activity, that could be a time-efficient way to increase PA and reach the national recommendations. Additionally, it could have a positive effect to body composition.

Methods
Based on longitudinal cohort study, active commuting modes and objectively measured PA were used to determine the influence of commuting mode to steps, aerobic steps, BMI and waist circumference. Linear regression models were fitted to test the associations between the change groups of commuting mode and the longitudinal changes of the response variables.

Results
When compared to passive commuters, participants with public transport (p = 0.09) and walking (p > 0.001-0.021) showed higher amounts of steps and aerobic during summertime and wintertime. Cyclers showed higher amounts of steps and aerobic steps only in wintertime (p = 0.001-0.002). Passive commuters had higher BMI than walkers (p = 0.05) and cyclers (p = 0.023) in summertime. Also, cyclers had lower waist circumference than passive commuters (p = 0.016-0.02). Among those who remained persistently active, number of steps did not change. When compared to persistently active, among those who changed from active to passive commuting, steps (-900 --885) and aerobic steps (-500) declined (p = 0.010-0.036) while among those who changed from passive to active commuting steps (+900-1000) and aerobic steps (+650-750) increased (p = 0.023-0.011).

Conclusions
Commuting actively to work and changing passive mode to active mode has a positive effect to number of daily steps and aerobic steps. Since the active commuting is part of the daily